Dynamic Graph Neural Networks for Sequential Recommendation
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Modeling users' preference from his historical sequences is one of the core problem of sequential recommendation. Existing methods in such fields are widely distributed from conventional methods to deep learning methods. However, most of them only model users' interests within their own sequences and ignore the fine-grained utilization of dynamic collaborative signals among different user sequences, making them insufficient to explore users' preferences. We take inspiration from dynamic graph neural networks to cope with this challenge, unifying the user sequence modeling and dynamic interaction information among users into one framework. We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects the sequence of different users through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information. Furthermore, we design a Dynamic Graph Attention Neural Network to achieve the information propagation and aggregation among different users and their sequences in the dynamic graph. Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction task for the user node to the item node in a dynamic graph. Extensive experiments on four public benchmarks show that DGSR outperforms several state-of-the-art methods. Further studies demonstrate the rationality and effectiveness of modeling user sequences through a dynamic graph.
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